基于类别特征提取的组合支持向量机模型  

Compositional support vector machine model based on feature extraction of categories

作　　者：吕业[1] 邓玉俊[1] 杨慧中[1,2] 

机构地区：[1]江南大学教育部轻工过程先进控制重点实验室,江苏无锡214122 [2]上海市电站自动化技术重点实验室,上海200072

出　　处：《化工学报》2011年第8期2164-2169,共6页CIESC Journal

基　　金：国家自然科学基金项目(60674092);江苏省高技术研究项目(BG2006010);上海市科学技术委员会(09DZ2273400)~~

摘　　要：在建立复杂生产过程软测量模型时,使用单一的支持向量机模型或基于传统聚类方法的组合支持向量机模型有时难以很好地跟踪突变信号或取得满意的泛化效果。为解决这个问题提出了一种改进的线性判别分析算法。该算法结合类边界分析得到类别的特征向量,利用该特征向量将数据变换后分别建立支持向量机子模型,并用各组特征向量中有效特征值之和构建各子模型的组合参数。仿真实验表明该组合模型能降低相邻类别间的信息干扰,提高模型的估计精度。When a soft sensor model is constructed for a complicated production process,a single support vector machine（SVM）model or a compositional SVM model based on conventional clustering methods sometimes cannot track mutant signal well or obtain a satisfactory generalization.An improved linear discriminant analysis（LDA）algorithm is proposed in this paper so as to solve the problem.The feature vectors are obtained by combining boundary analysis with LDA between the categories.The original sample data are transformed in terms of the feature vectors,and sub-models based on SVM are respectively constructed by transformed data.And then the compositional parameters for sub-models are designed according to the sum of the effectual characteristic values in the every feature vector.Finally,a compositional SVM model is constructed.The simulation results show that the composition model can reduce the information interference among the different data categories and improve the inferential accuracy of the model.

关 键 词：线性判别分析 组合支持向量机 分类 特征提取 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]